Ideas

All articles
3 Nov 2026Autonomous agents: what they are and how they workThis guide explains what autonomous agents are, how they plan, use tools, remember, and check their work, and how people use them for research, email, coursework, and shopping.
2 Nov 2026AI agent memory: How to build AI agents with persistent contextThis guide explains what AI agent memory is, how working, episodic, semantic, and procedural memory work together, and how to build and test agents that improve through persistent context.
1 Nov 2026The Knowledge Talk
30 Oct 2026How to Fine-Tune an LLM [and When to Use RAG or Prompting Instead]This guide explains how to fine-tune an LLM step by step, compares full fine-tuning with LoRA and QLoRA, and shows when prompting or RAG is the better way to improve a model's outputs.
29 Oct 2026Text embeddings explained: how they work [+examples]This guide explains what text embeddings are, how they turn text into vectors that capture meaning, and how to choose, evaluate, and apply embedding models to search, RAG, and other real-world use cases.
28 Oct 2026Basic RAG Implementation: A Step-by-Step TutorialThis beginner-friendly tutorial explains how a basic RAG implementation works, covers best practices, and walks through building a working command-line RAG system with Python, ChromaDB, and Perplexity's embedding and language models.
27 Oct 2026How does an LLM gateway work? A practical guide for engineering teamsAn LLM gateway is a reverse proxy that routes, authenticates, caches and logs your model calls; this guide follows a request through one, covers the security and reliability risks to plan for, and compares building, self-hosting, managed and API-gateway options.
26 Oct 2026What Is RAG Architecture? A Full Definition With Detailed Practical ExamplesRAG architecture is the set of components that stores, retrieves, and feeds external information to an LLM; this guide breaks down 10 types, from basic and hybrid RAG to agentic RAG and GraphRAG, with two step-by-step examples.
22 Oct 2026How to evaluate AI agents: test sets, graders and regression checksA practical six-step guide to evaluating AI agents, from defining success and building test sets from real failures to choosing graders, running regression checks and monitoring in production.